Prediction of pseudoprogression and long-term outcome of vestibular schwannoma after Gamma Knife radiosurgery based on preradiosurgical MR radiomics

Huai Che Yang, Chih Chun Wu, Cheng Chia Lee, Huai En Huang, Wei Kai Lee, Wen Yuh Chung, Hsiu Mei Wu, Wan Yuo Guo, Yu Te Wu, Chia Feng Lu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

Background and purpose: Gamma Knife radiosurgery (GKRS) is a safe and effective treatment modality with a long-term tumor control rate over 90% for vestibular schwannoma (VS). However, numerous tumors may undergo a transient pseudoprogression during 6–18 months after GKRS followed by a long-term volume reduction. The aim of this study is to determine whether the radiomics analysis based on preradiosurgical MRI data could predict the pseudoprogression and long-term outcome of VS after GKRS. Materials and methods: A longitudinal dataset of patients with VS treated by single GKRS were retrospectively collected. Overall 336 patients with no previous craniotomy for tumor removal and a median of 65-month follow-up period after radiosurgery were finally included in this study. In total 1763 radiomic features were extracted from the multiparameteric MRI data before GKRS followed by the machine-learning classification. Results: We constructed a two-level machine-learning model to predict the long-term outcome and the occurrence of transient pseudoprogression after GKRS separately. The prediction of long-term outcome achieved an accuracy of 88.4% based on five radiomic features describing the variation of T2-weighted intensity and inhomogeneity of contrast enhancement in tumor. The prediction of transient pseudoprogression achieved an accuracy of 85.0% based on another five radiomic features associated with the inhomogeneous hypointensity pattern of contrast enhancement and the variation of T2-weighted intensity. Conclusion: The proposed machine-learning model based on the preradiosurgical MR radiomics provides a potential to predict the pseudoprogression and long-term outcome of VS after GKRS, which can benefit the treatment strategy in clinical practice.

Original languageEnglish
Pages (from-to)123-130
Number of pages8
JournalRadiotherapy and Oncology
Volume155
DOIs
StatePublished - Feb 2021

Keywords

  • Gamma Knife radiosurgery
  • Machine learning
  • Magnetic resonance image
  • Radiomics
  • Vestibular schwannoma

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